ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Mixing Natural Language Processing and Image Segmentation for Pre-Diagnosis in Medicine. Application to Breast Cancer

3 September 2018, 14:40 – 15:00


Submitted by
Nicolas Bousquet
Stéphane Jankowski (Quantmetry), Pablo Valverde (Quantmetry), Antoine Simoulin (Quantmetry), Nicolas Bousquet (Quantmetry), Sébastien Molière (Hôpitaux Universitaires de Strasbourg), Carole Mathelin (Hôpitaux Universitaires de Strasbourg)
We consider a case where a physician has to radically diminish the time required for a pre-operational diagnosis which can be established with the simultaneous help of an ontology based on electronic health records (EHR) and the localization of particular shapes using medical imaging. In the case of breast cancer, the search for tumors using three-dimensional magnetic resonance imaging (MRI) can be automatized using deep learning algorithms. However, its uses remains sometimes controversial, since leading possibly to confusions which can has major impacts on surgery choices. We propose an optimization of machine learning techniques applied to MRI by putting into coherence its results with an automated building of this ontology, based on natural language processing of EHR and the elicitation of a French dictionary of medical synonyms. The full methodology is presented in this talk, with details on the problems raised by name matching and the optimization process itself

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